86 results about "Sensor model" patented technology

Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.

Model selection is disclosed. Features used as inputs to models are scored in terms of importance and health. The importance and health scores are combined in order to generate a model score for each model. The model with a score above a threshold score is selected and deployed.

The invention provides an automatic driving live-action simulator based on three-dimensional Gaussian sputtering, and the simulator comprises a general model interface module which achieves the integration of different Gaussian models, sensor models and vehicle kinematics models through providing a standardized interface, and obtains basic elements and self-vehicle elements in a simulation scene; the internal scene representation module is used for defining and organizing the basic elements in a structured mode to obtain environment elements; the collision detection module is used for distinguishing foreground objects and backgrounds of the environment elements, respectively carrying out foreground collision detection and background collision detection, and carrying out collision judgment on the self-vehicle elements and the environment elements in the simulation scene; and the simulation control module is used for performing dynamic response of the simulator to the external control signal based on the internal scene representation module and the collision detection module. The method can meet the requirements of the automatic driving algorithm for massive high-quality data, and is suitable for training and testing of the automatic driving algorithm.

The application discloses a kind of HIL simulation methods of underground mining whole process, it is related to the field of autonomous driving simulation test, including: the point cloud data of underground scene is collected, and CARLA format simulation scene is generated;The generated CARLA format simulation scene is compiled and imported by Unreal engine, vehicle model and laser radar sensor model are built in Carla simulator, and the point cloud data packet of simulation scene is collected;According to the point cloud data packet, mapping is carried out by SLAM algorithm, and the point cloud map of PCD format is generated;According to the PCD point cloud map, the map of simulation scene is constructed;Hardware-in-the-loop HIL simulation platform including host computer, lower computer and actuating mechanism is built;The map of simulation scene constructed is loaded to the hardware-in-the-loop HIL simulation platform constructed, and the real mine working condition scene is simulated through simulation platform, and the mine working condition unmanned driving algorithm is verified.The present application improves the coverage of test by whole process closed loop etc., for the difficulty of comprehensive evaluation unmanned driving test in prior art.

Method for evaluating a data-based sensor model (5) for determining a change-point time in a sensor signal time series (S), comprising the following steps: - Providing (S2) an evaluation signal time series (A) in an evaluation time window of a sensor signal time series (S); - Determining (S3) sensor signal segments (F1, F2, F3, F4) from the evaluation signal time series (A) that are temporally shifted or offset from each other by a number of sampling steps, wherein the sensor signal segments (F1, F2, F3, F4) have a shorter length than the evaluation signal time series; - Determining (S4) one or more frequency contributions from the sensor signal segments (F1, F2, F3, F4) using an FFT or a Goertzel algorithm; - Evaluating (S5, S6) the frequency contributions in a trained data-based sensor model (5) to determine a change-point time within the evaluation time window.

The application discloses a kind of scanning path planning methods, equipment, medium and product for aircraft upper surface, which comprises: based on curvature, normal vector and point cloud density joint detection algorithm, the overall point cloud data of the aircraft to be scanned is divided into data, to obtain each target point cloud partition corresponding to each target functional partition in the upper surface of the aircraft, after rectification treatment along the direction of normal vector, generate initial candidate viewpoint set and screen out safe viewpoint set.According to the visibility matrix constructed by the sensor model, the optimal viewpoint set is screened out from the safe viewpoint set using the greedy algorithm, and at least one optimal access sequence is solved using a hybrid solution strategy.The scanning path corresponding to each optimal access sequence is constructed and respectively issued to the unmanned aerial vehicle.The technical scheme can improve the detection efficiency and safety of the upper surface of the aircraft, and enhance the degree of automation and detection stability of the system.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The invention provides an alumen ustum detection dosing control method, system and equipment based on visual identification, and belongs to the technical field of water treatment. According to the method, water inlet parameters, water outlet parameters and alumen ustum images of a clarification tank are collected in real time, and alumen ustum characteristic parameters including the number of alumen ustum, the area proportion, the equivalent diameter, the circularity and the fractal dimension are extracted; performing time sequence alignment on the collected data to enable the related data and the alumen ustum characteristic parameters to correspond to the same batch of water flow, and associating with the dosing amount to obtain dosing time sequence data; inputting the dosing time sequence data into a pre-trained multi-layer sensor model to obtain a target dosing amount of next dosing; and the dosing flow is calculated according to the target dosing amount and the real-time concentration of the medicament, and the opening degree of the flow valve is controlled through a PID algorithm. By means of the technical scheme, intelligent association of alumen ustum characteristics and the dosing amount is achieved, and the accuracy and automation degree of dosing control in the water treatment process are improved.

The invention discloses an unmanned aerial vehicle license plate recognition simulation platform based on ROS and a construction method of the unmanned aerial vehicle license plate recognition simulation platform. The method comprises the steps of 1, building a scene, an unmanned aerial vehicle model, a sensor model and flight control; step 2, releasing the sensor data of the virtual scene through ROS; 3, a PX4 flight control instruction is converted into physical motion of the unmanned aerial vehicle model through a PX4-Gazebo simulation system, flight state data is obtained, and deployment and verification of a flight control algorithm are achieved; 4, completing the development and integration of a license plate recognition function, and obtaining recognition result data; and 5, realizing the display of the multi-dimensional data. According to the invention, the flight state and the recognition effect of the unmanned aerial vehicle during execution of the license plate recognition task can be simulated efficiently.

The invention relates to the technical field of Internet of Things data interaction, in particular to a data interaction method based on Internet of Things equipment acquisition, which comprises the following steps: S1, acquiring non-electric quantity data of a ring main unit by an Internet of Things sensor, and accessing the non-electric quantity data into a ring main unit DTU through a wireless receiving device via a Modbus protocol to realize software and hardware fusion; s2, the DTU carries out timestamp alignment and format standardization on the non-electric quantity data and electric quantity data collected by the DTU, generates an integrated data packet conforming to a DL / T634.5101 protocol and marks the priority; s3, performing hierarchical encryption on the data based on the priority; s4, the DTU uploads the encrypted data to the distribution automation master station according to the priority; s5, the master station decrypts and analyzes the data based on the data point table, and pushes the data to the dynamically expanded sensor model for correlation analysis; and S6, realizing intelligent study and judgment, health evaluation release and abnormity alarm of the ring main unit. The problems of heterogeneous access of data of the ring main unit, unbalanced transmission efficiency and safety, poor expansibility and the like are solved, and the intelligent operation and maintenance level of the power distribution network is improved.

The present disclosure relates to the field of bio-information technologies, in particular to a single-device multi-physiological-parameter synchronization and fusion method and a multi-device multi-physiological-parameter synchronization device. The single-device multi-physiological-parameter synchronization and fusion method includes the following steps: constructing a one-to-one wireless transmission system; constructing a one-to-many wired transmission system; initiating a synchronization instruction; checking information; matching information to align first collection data packets of the sensor modules. In the present disclosure, the master collection control module sends synchronization information to multiple sensor modules at the same time, and the master collection control module, based on multiple pieces of timestamp information, maps the collection data packets sent from the multiple sensor modules by time mapping, without requiring multiple sensor modules to be series-connected through a same clock source. In this case, the sensor models are not limited and real-time adjustment can be made to the sampling rate, ensuring high synchronization efficiency between multiple sensor modules.

The invention relates to a vehicle for transporting goods and / or people on a right-hand or left-hand drive road, comprising, for a given exteroceptive sensor model, a single sensor of that model positioned on a front part of the vehicle to obtain information about the environment in which the vehicle is located, referred to as the single front sensor. In the case of right-hand drive, respectively left-hand drive, the single front sensor is offset to the left, respectively to the right, by a median sagittal plane separating a left part from a right part of the vehicle.

A simulation apparatus comprises a model storage unit storing a physical model of a physical system configured to generate a physical signal waveform, a sensor model, a machine learning model, and an abnormality determination model; a model arithmetic unit configured to perform arithmetic processing using the physical model, the sensor model, the machine learning model, and the abnormality determination model; and a model setting unit configured to perform setting related to each of the physical model, the sensor model, the machine learning model, and the abnormality determination model, on the basis of an input from an operation input unit.

The invention belongs to the technical field of airplane structure strength tests, and particularly relates to a three-dimensional definition method and device for airplane structure static strength test data. The method comprises the following steps: step 1, extracting sensor space calibration information; 2, constructing a digital model of the test object; 3, searching a grid closest to the centroid of the sensor in the digital model of the test object according to the space calibration information of the sensor, and constructing a sensor model; 4, performing association mapping on the test data, the sensor model and the digital model of the test object; and 5, performing data analysis, and displaying a data analysis result on the sensor model and the test object digital model. According to the method, three-dimensional definition can be performed on the test data on the sensor model and the test object digital model, so that management, analysis evaluation and visual interaction are performed on the full-cycle test data based on the test object digital model, and the utilization rate and analysis efficiency of the test data are improved.

The invention discloses a sensor overturn compensation method, system and device based on range prediction, and a storage medium. The method comprises the following steps: configuring a range upper limit threshold and an upper / lower limit threshold according to a sensor model; receiving a data message containing an accumulated value, a message ID and a timestamp; performing repeated judgment by comparing the message ID with the timestamp; if the message is not repeated, performing smooth filtering on the accumulated value, then performing trend analysis in combination with sliding window statistics, and when a transition mode from a continuous high order to a continuous low order is detected, judging overturning; updating a turnover frequency counter of the sensor, and calculating a real accumulated value after compensation according to the turnover frequency, the upper limit of the measuring range and the current accumulated value; and finally updating the cache state. According to the invention, automatic and accurate detection and real-time compensation of data overturning of the accumulation type instrument are realized, and the automation level, accuracy and reliability of data processing are effectively improved.

A method and system for creating a high-fidelity depiction of a scene including an object within the scene are provided. The method includes uniformly illuminating a target surface of the object with light to obtain reflected, backscattered illumination. The method also includes sensing via a volumetric sensor, brightness of the surface due to a diffuse component of the backscattered illumination to obtain brightness information. Backscattered illumination from the target surface is inspected to obtain geometric measurements which include sensor noise. Rotationally and positionally invariant measured surface albedo including albedo noise of the object is computed based on the brightness and the geometric measurements. A machine-learning model such as a diffusion sensor model is applied to the geometric measurements and the measured surface albedo to remove the sensor noise and the albedo noise, respectively, to obtain a prediction of actual geometry and actual albedo, respectively, of the object.

The application discloses a device signal level simulation integrated environment construction carrier platform, which can improve simulation efficiency, device signal level simulation process and result fidelity. The application is realized through the following technical means: a modeling and scene management unit establishes a carrier platform and a functional sensor model based on a signal level of a scene, connects a radio frequency signal interface of a semi-physical radio frequency system or device to a radio frequency signal interface of a multifunctional signal modulation device, and forms a radio frequency closed loop; a simulation control and visual unit loads a scenario, generates time sequence data of track points of each carrier platform, deduces a simulation process, and distributes the scenario, the track points of the carrier platform and simulation clock data to each component unit; an environment simulation unit internally arranges electromagnetic simulation calculation software to calculate corresponding transmission time delay, phase, Doppler frequency shift and power attenuation modulation parameters, and sends the parameters to the multifunctional signal modulation device; and a simulation function unit calls a bottom layer driving code to send function results and state data through Ethernet.

A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.

A path planning method for multi-unmanned aerial vehicle collaborative search comprises the steps that firstly, an unmanned aerial vehicle kinematics model, a sensor model and an environment cognition map are established, and a target existence probability and environment uncertainty map is updated in real time based on the Bayesian theorem; secondly, constructing a model-based prediction control path decision framework, predicting a future multi-step path through rolling optimization, and generating a control sequence; in the optimization solution, a double-population three-layer competition mechanism is adopted, particle population individuals are divided into a loser, a candidate and a winner, and differential updating strategies are adopted to balance the global exploration and local utilization ability of particles; meanwhile, a feedback mechanism based on particle evolution quality is introduced, the size of the sub-population is dynamically adjusted, and population diversity is maintained in combination with periodic recombination. According to the method, the solving quality of the search path planning problem based on model prediction control is remarkably improved, meanwhile, the high search coverage rate is guaranteed, and the method is suitable for multi-unmanned-aerial-vehicle cooperative search scenes such as disaster rescue and environment monitoring.

A method of modeling three-dimensional objects from two-dimensional imagery includes receiving, from an imaging device with a sensor, one or more digital images of an object and image metadata representing a focal length of the sensor, a pixel size of the sensor, and a range of each of the one or more digital images; adding, to the one or more digital images, digital markings representing perpendicular axes of the object; computing at least one sensor model of the imaging device based on the image metadata, the digital markings, and the perpendicular axes of the object; computing object-space coordinates of points corresponding to different features of the object based on the at least one sensor model; and generating a three-dimensional model of the object based on the object-space coordinates of the points.

The application discloses an interference compatibility method for an NGSO-FSS system and an adjacent frequency satellite system, and belongs to the technical field of radio communication frequency compatibility.The method solves the problem of interference between the NGSO-FSS system and the adjacent frequency satellite system, and comprises the following steps: at the satellite layer, designing an NGSO constellation configuration and a sensor model of the adjacent frequency satellite system; at the ground end, determining the position and quantity of gateway stations in the NGSO-FSS system; at the link layer, regarding the gateway stations of the NGSO-FSS system as interference sources, considering the total atmospheric attenuation and the free space transmission loss, obtaining the interference power received by the adjacent frequency satellite system, and further adjusting the transmission power of the corresponding antennas of the gateway stations; based on the gateway stations and the NGSO constellation, evaluating the total interference of all active gateway stations for the adjacent frequency satellite system, calculating the interference reduction amount, adjusting the useless transmission power and the NGSO constellation configuration according to the interference reduction amount, and until the interference power level meets the threshold value of the protection criterion.

This invention relates to the field of shock wave pressure measurement technology and discloses a dynamic compensation method for shock wave pressure sensors based on PINN. The method includes: obtaining step response data of the pressure sensor through shock tube calibration experiments and establishing a dynamic characteristic model of the sensor; constructing a physical information neural network, embedding the sensor dynamic characteristic model as a physical layer into the network structure; designing a composite loss function including data loss terms and physical constraint loss terms, and training the physical information neural network based on simulation data; inputting the distorted pressure signal to be corrected into the trained network, and outputting the corrected shock wave pressure signal. This invention achieves signal correction without requiring precise sensor model parameters and can recover the true pressure waveform that conforms to physical laws under strong noise conditions.

The application discloses a kind of field knowledge auxiliary ground multi-target air monitoring methods, comprising the following steps: S1 establishes target sensor model, establishes field knowledge set;S2 according to the target information obtained by sensor, matches target corresponding field knowledge;S3 according to the target information obtained by sensor, utilizes target sensor model, using improved JPDA method carries out multi-target tracking, in the tracking process, the state estimation value of model is corrected by field knowledge;S4 updates target corresponding field knowledge;repeat steps S3-S4, continuously track target.The field knowledge auxiliary ground multi-target air monitoring method disclosed in the application improves the multi-target tracking precision while enhancing the robustness of target tracking and trajectory updating process.

The application discloses a modeling method for a nitrogen oxygen sensor NOx pump unit, and belongs to the field of automobile sensors. The method comprises the following steps: considering the reduction reaction of NO and O2 on an electrode to establish an electrochemical sub-model of the NOx pump unit, wherein the electrochemical sub-model comprises Nernst voltage, cathode and anode concentration loss, cathode and anode activation loss and Ohm loss; considering gas diffusion and early decomposition of NO in a second chamber to establish a gas diffusion sub-model of the NOx pump unit, wherein the gas diffusion sub-model comprises diffusion of gas in a diffusion channel and diffusion of gas in the electrode; combining the electrochemical sub-model and the gas diffusion sub-model through a limiting current to obtain a combined model, and the combined model is used for parameter setting and working control of the nitrogen oxygen sensor. Through the combined model which considers all characteristics in the pump unit and eliminates the intermediate variable of gas concentration, the precision and calculation speed of the sensor model are improved.

The application discloses a DPF carbon load model correction method and device, electronic equipment and medium. The DPF carbon load model correction method comprises the following steps: in the process of engine operation, the DOC front temperature sensor measured value and the DPF front temperature sensor measured value are acquired in real time, and it is judged whether they are credible; when it is judged that both are not credible, the DOC front temperature sensor model value or the DPF front temperature sensor model value is input into the DPF carbon load model, and the current DPF carbon carrying capacity is output; after determining that the differential pressure carbon load is highly credible, whether the DPF carbon load model is corrected is judged according to the current differential pressure carbon load and the current DPF carbon carrying capacity, and when it is judged that the DPF carbon load model is corrected, the DPF carbon load model is corrected based on the current differential pressure carbon load. The application realizes improving the precision of the DPF carbon load model, and avoids the frequent regeneration or DPF overload risk caused by the inaccuracy of the DPF carbon load model.

A lunar surface digital environment construction simulation system, a moon-earth space-time reference simulation module is used for providing a space-time reference for the whole simulation system and a conversion relationship between different space-time references; a moon surface data resource management module is used for generating a lunar surface multi-scale grid data by using remote sensing data and image data through alignment and multi-level division processing, for real-time dynamic loading according to a viewpoint position; a lunar surface comprehensive environment simulation module is used for modeling a lunar surface illumination, a starry sky background, a ground surface material, a topographic feature and a lunar dust dispersion; a simulation task working condition management module is used for establishing a detector model and a sensor model, and performing sensor configuration according to a requirement, providing data for the sensor and acquiring data output by the sensor; a lunar surface scene rendering demonstration module is used for rendering a scene image including a moon surface data, a lunar surface comprehensive environment and a detector model.

This application proposes a method, apparatus, electronic equipment, and program product for constructing and integrating test models for legged robots. First, a data acquisition terminal and a scene modeling configuration module generate a scene model file to build a standardized test scene. A sensor modeling configuration module then generates a sensor simulation model file coupled with the environment based on this file, simulating the robot's perception function. A dynamics modeling configuration module combines the robot's basic information with scene characteristics to generate a validated dynamics model file, simulating the robot's motion function. An integrated testing module loads the three types of models to complete collaborative simulation testing, achieving collaborative verification of perception and motion functions. Finally, the test process configuration is updated to achieve standardized construction and integrated testing of the test model. By relying on standardized models to optimize the testing process, the standardization and adaptability of legged robot simulation testing are improved, providing reliable support for its testing.

This invention relates to the field of spacecraft mission simulation and intelligent control technology, specifically disclosing a system and method for modeling and simulating spacecraft assembly scenarios. The invention constructs a coupled dynamics simulation module encompassing orbital dynamics, attitude dynamics, and robotic arm dynamics; builds a visual simulation module integrating multiple types of sensor models based on Unreal Engine; and employs the ROS2 communication framework to achieve real-time data interaction with external algorithm platforms. This solves the problems of lack of physical realism, real-time interaction capabilities, and open interfaces in existing space assembly simulation technologies. It achieves high-fidelity dynamic simulation of multi-spacecraft collaborative operations, deformation of large flexible structures, and precise control of robotic arms, enhancing the full-chain simulation support capabilities for space assembly missions from scenario construction and dynamics calculation to control algorithm verification.

Simulation device (1) comprising: a virtual space generation unit (111) that generates a virtual space representing a workspace in three dimensions; a measurement object model arrangement unit (112) that arranges a measurement object model representing a measurement object in three dimensions within the virtual space; a measurement point identification unit (114) that identifies or specifies a measurement point in the measurement object model; a visual sensor model arrangement unit (115) that arranges a visual sensor model representing a visual sensor depicting the measurement object in three dimensions at an arbitrary visual sensor model position; a position determination unit (120) that determines the position of the visual sensor model contained in an image size of the visual sensor model as an arrangement position of the visual sensor model based on a position of the measurement point;and a position conversion unit (119) that converts a three-dimensional position of the measurement point in the position of the visual sensor model into a two-dimensional position when the measurement point is mapped by the visual sensor model, wherein the position determination unit (120) determines the position of the visual sensor model in which all two-dimensional positions are contained in one image size of the visual sensor model as an arrangement position of the visual sensor model.

This application discloses a modeling method, apparatus, vehicle, and storage medium for a vehicle attitude sensor model. The method includes: solving for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and the automotive-grade sensor; obtaining the variation law of the difference between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor based on the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate; and calculating the longitudinal acceleration value, lateral acceleration value, and / or yaw rate data considering the steady-state deviation and noise deviation of the automotive-grade sensor based on the variation law, in order to model the vehicle attitude sensor model. This solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, resulting in low accuracy in the calculation results of the vehicle attitude.